{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def qsnr(orig, quant):\n",
    "    \"\"\"Calculate the QSNR between two tensors\n",
    "    \"\"\"\n",
    "    qerr = orig - quant\n",
    "    sum_err = np.sum(qerr * qerr)\n",
    "    sum_orig = np.sum(orig * orig)\n",
    "    if sum_err > 0:\n",
    "        if sum_orig < sum_err:\n",
    "            if sum_orig == 0:\n",
    "                return -math.inf\n",
    "            else:\n",
    "                # Means error is larger than signal\n",
    "                return -int(round(10 * math.log10(sum_err/sum_orig), 0))\n",
    "        # Error portion of signal\n",
    "        return int(round(10 * math.log10(sum_orig/sum_err), 0))\n",
    "    # Means no error\n",
    "    return math.inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
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    "In_Data = In_Data[::2] + 1j*In_Data[1::2]\n",
    "In_Data = np.array([0.848657+0.000000j, 0.711346+0.000000j, 0.527794+0.000000j, 0.319074+0.000000j, 0.108742+0.000000j, -0.080641+0.000000j, -0.230779+0.000000j, -0.330162+0.000000j, -0.375452+0.000000j, -0.371525+0.000000j, -0.330190+0.000000j, -0.267841+0.000000j, -0.202445+0.000000j, -0.150405+0.000000j, -0.123821+0.000000j, -0.128616+0.000000j, -0.163824+0.000000j, -0.222128+0.000000j, -0.291527+0.000000j, -0.357811+0.000000j, -0.407378+0.000000j, -0.429874+0.000000j, -0.420196+0.000000j, -0.379467+0.000000j, -0.314842+0.000000j, -0.238161+0.000000j, -0.163696+0.000000j, -0.105421+0.000000j, -0.074276+0.000000j, -0.075953+0.000000j, -0.109607+0.000000j, -0.167744+0.000000j, -0.237340+0.000000j, -0.302012+0.000000j, -0.344884+0.000000j, -0.351648+0.000000j, -0.313298+0.000000j, -0.228040+0.000000j, -0.102029+0.000000j, 0.051228+0.000000j, 0.212722+0.000000j, 0.360776+0.000000j, 0.474396+0.000000j, 0.536602+0.000000j, 0.537174+0.000000j, 0.474376+0.000000j, 0.355348+0.000000j, 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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "OutFFT64_f32 = np.array([\n",
    "-4.281758+0.000000j, 3.433894+4.899633j, 3.456662-4.017673j, 8.782061-0.440074j, 3.537963+5.973732j, 0.544670-3.195469j, 0.589646-1.611792j, 0.525506-1.133393j, 0.474162-0.887159j, 0.437046-0.731699j, 0.410031-0.622183j, 0.389929-0.539560j, 0.374638-0.474208j, 0.362763-0.420687j, 0.353381-0.375658j, 0.345854-0.336954j, 0.339742-0.303098j, 0.334723-0.273025j, 0.330564-0.245973j, 0.327099-0.221354j, 0.324189-0.198729j, 0.321744-0.177745j, 0.319680-0.158122j, 0.317941-0.139623j, 0.316483-0.122067j, 0.315260-0.105295j, 0.314250-0.089160j, 0.313432-0.073553j, 0.312779-0.058365j, 0.312287-0.043501j, 0.311948-0.028874j, 0.311745-0.014396j, 0.311674+0.000000j, 0.311745+0.014396j, 0.311947+0.028874j, 0.312287+0.043501j, 0.312778+0.058365j, 0.313432+0.073553j, 0.314250+0.089160j, 0.315260+0.105295j, 0.316483+0.122067j, 0.317941+0.139623j, 0.319680+0.158122j, 0.321744+0.177745j, 0.324189+0.198729j, 0.327099+0.221354j, 0.330564+0.245973j, 0.334723+0.273025j, 0.339742+0.303098j, 0.345854+0.336954j, 0.353381+0.375658j, 0.362763+0.420687j, 0.374638+0.474208j, 0.389929+0.539560j, 0.410031+0.622183j, 0.437046+0.731699j, 0.474162+0.887159j, 0.525506+1.133394j, 0.589646+1.611792j, 0.544670+3.195470j, 3.537962-5.973732j, 8.782059+0.440074j, 3.456661+4.017673j, 3.433894-4.899633j, ])\n",
    "OutFFT64_f32 = np.array([\n",
    "-4.281758+0.000000j, 3.433894+4.899633j, 3.456662-4.017673j, 8.782061-0.440074j, 3.537963+5.973732j, 0.544670-3.195470j, 0.589646-1.611792j, 0.525506-1.133394j, 0.474162-0.887159j, 0.437046-0.731699j, 0.410031-0.622183j, 0.389929-0.539560j, 0.374638-0.474208j, 0.362763-0.420687j, 0.353381-0.375658j, 0.345854-0.336954j, 0.339742-0.303098j, 0.334723-0.273025j, 0.330564-0.245973j, 0.327099-0.221354j, 0.324189-0.198729j, 0.321744-0.177745j, 0.319680-0.158122j, 0.317941-0.139623j, 0.316483-0.122067j, 0.315260-0.105295j, 0.314250-0.089160j, 0.313432-0.073553j, 0.312778-0.058365j, 0.312287-0.043501j, 0.311947-0.028874j, 0.311745-0.014396j, 0.311674+0.000000j, 0.311745+0.014396j, 0.311947+0.028874j, 0.312287+0.043501j, 0.312778+0.058365j, 0.313432+0.073553j, 0.314250+0.089160j, 0.315259+0.105295j, 0.316483+0.122067j, 0.317941+0.139623j, 0.319680+0.158122j, 0.321744+0.177745j, 0.324189+0.198729j, 0.327099+0.221354j, 0.330564+0.245974j, 0.334723+0.273025j, 0.339742+0.303098j, 0.345854+0.336954j, 0.353381+0.375658j, 0.362763+0.420687j, 0.374638+0.474208j, 0.389929+0.539560j, 0.410031+0.622183j, 0.437046+0.731699j, 0.474162+0.887159j, 0.525506+1.133394j, 0.589646+1.611792j, 0.544670+3.195470j, 3.537963-5.973732j, 8.782059+0.440074j, 3.456662+4.017673j, 3.433894-4.899633j, ])\n",
    "\n",
    "OutFFT64_q16 = np.array([\n",
    "-2192+0j, 1748+2498j, 1761-2059j, 4485-231j, 1807+3053j, 275-1639j, 298-830j, 264-583j, 241-453j, 221-377j, 210-320j, 199-278j, 188-245j, 183-217j, 177-193j, 174-174j, 173-156j, 170-142j, 170-126j, 166-116j, 165-101j, 164-90j, 164-82j, 163-72j, 161-63j, 159-55j, 159-47j, 159-40j, 157-30j, 158-24j, 157-17j, 156-8j, 160+0j, 160+8j, 159+15j, 159+23j, 159+31j, 161+39j, 160+46j, 162+55j, 163+63j, 163+71j, 164+82j, 163+92j, 166+101j, 169+113j, 167+123j, 170+140j, 173+156j, 176+170j, 180+192j, 184+214j, 191+243j, 198+276j, 208+316j, 221+374j, 241+451j, 267+579j, 301+823j, 277+1632j, 1807-3054j, 4492+222j, 1765+2053j, 1754-2502j, ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marco-gwt/anaconda3/envs/tf2.3/lib/python3.8/site-packages/numpy/core/_asarray.py:83: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/home/marco-gwt/anaconda3/envs/tf2.3/lib/python3.8/site-packages/numpy/core/_asarray.py:83: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/home/marco-gwt/anaconda3/envs/tf2.3/lib/python3.8/site-packages/numpy/core/_asarray.py:83: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7bc0f794f0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.fft.fft(In_Data[:64]))\n",
    "plt.plot(OutFFT64_f32)\n",
    "plt.plot(OutFFT64_q16*2**(-9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
